Bayesian estimation in random effects meta‐analysis using a non‐informative prior
Abstract
Pooling information from multiple, independent studies (meta‐analysis) adds great value to medical research. Random effects models are widely used for this purpose. However, there are many different ways of estimating model parameters, and the choice of estimation procedure may be influential upon the conclusions of the meta‐analysis. In this paper, we describe a recently proposed Bayesian estimation procedure and compare it with a profile likelihood method and with the DerSimonian–Laird and Mandel–Paule estimators including the Knapp–Hartung correction. The Bayesian procedure uses a non‐informative prior for the overall mean and the between‐study standard deviation that is determined by the Berger and Bernardo reference prior principle. The comparison of these procedures focuses on the frequentist properties of interval estimates for the overall mean. The results of our simulation study reveal that the Bayesian approach is a promising alternative producing more accurate interval estimates than those three conventional procedures for meta‐analysis. The Bayesian procedure is also illustrated using three examples of meta‐analysis involving real data. Copyright © 2016 John Wiley & Sons, Ltd.
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A Bayesian estimation framework more naturally accounts for all parameter uncertainty in the derivation of credibility and probability intervals.
“which more naturally accounts for all parameter uncertainty in the derivation of credibility and probability intervals.”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [52]
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- Authors
- Bodnar, O., Link, A., Arendacká, B., Possolo, A., & Elster, C.
- Affiliations
- Physikalisch-Technische Bundesanstalt (DE)
- Institut für Medizinische Statistik Humboldtallee 32 Göttingen Germany
- National Institute of Standards and Technology (US)
- Corresponding author
- Bodnar, Olha
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2016-10-28
- Volume
- 36
- Issue
- 2
- Pages
- 378-399
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2544437330
- Cited by
- 73
- References
- 67
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 12 · 2022: 5 · 2021: 12 · 2020: 12 · 2019: 4 · 2018: 11 · 2017: 5 · 2016: 1
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Professor Louise Ryan Amanda Koepke (NIST)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- random-effects meta-analysis, Bayesian estimation, non-informative priors, study heterogeneity, profile likelihood, interval estimates, frequentist properties
- MeSH terms
- Bayes Theorem, Computer Simulation, Humans, Respiratory Tract Infections, Meta-Analysis as Topic, Models, Statistical, Stents, Confidence Intervals, Likelihood Functions, Acupuncture, Ear, Biostatistics
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